Our object relation model initially uses the ResNet-18 model of [1]}. As before, we make two changes to this model, we use a stride of \((2, 2, 2)\)  for the first convolutional layer, and we do not use the BatchNorm layers. The convolutional layer output is flattened into a vector of size 1536 which is then projected through 2 fully-connected layers with output size 512 and 256. Thus, we get the final relational embedding of size 256. We concatenate this with the action vector and pass them through three fully connected layers of size 128, 64 and 9 respectively. The final output of size 9 consists of the predicted position changes of size 3, predicted orientation changes of size 3 and predicted mean contact position of size 3.
